应用高斯粒子群优化的无迹粒子滤波

发布时间：2018-04-04 16:30

本文选题：无迹粒子滤波　切入点：高斯粒子群优化　出处：《测绘通报》2017年04期

【摘要】：针对粒子滤波算法中存在的粒子退化与粒子匮乏的缺陷,提出了利用高斯粒子群优化无迹粒子滤波的新算法。算法使用无迹粒子滤波进行重要性采样,并将高斯粒子群优化算法融入重采样过程中。该算法选取的概率密度更加接近系统真实状态,有效增加了粒子的多样性,提高了抽样效率,降低了粒子退化程度,缓解了粒子匮乏现象。试验结果表明,该算法的滤波精度明显优于粒子滤波与无迹粒子滤波算法所得到的滤波精度。
[Abstract]:Aiming at the defects of particle degradation and particle scarcity in particle filter algorithm, a new unscented particle filter algorithm based on Gao Si particle swarm optimization is proposed.The algorithm uses unscented particle filter for importance sampling, and Gao Si particle swarm optimization algorithm is incorporated into the resampling process.The probability density selected by the algorithm is closer to the real state of the system, which effectively increases the diversity of particles, improves the sampling efficiency, reduces the degree of particle degradation and alleviates the phenomenon of particle scarcity.The experimental results show that the filtering accuracy of this algorithm is better than that of particle filter and unscented particle filter.
【作者单位】：武汉大学卫星导航定位技术研究中心;
【基金】：国家重点基础研究发展计划(2013CB733205) 武汉市科技局项目(2015011701011639)
【分类号】：P207

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